SDS-051: Context Analyzer Service

[!WARNING] Superseded by SDS-051: Universal Context Service

This specification has been merged with SDS-051 (Project Context Analyzer) into a unified “Universal Context Service” that provides holistic context vectors for the Isomorphic Compiler.

Addresses Requirements

• PRD-009: Context-Aware Recommendations
• PRD-013: Context-Aware AI Behavior

Related ADRs

• ADR-005: Architectural Governance with CALM
• ADR-007: Cognitive Extension Layer

MVP Status

✅ MVP

Component Description

The Context Analyzer processes real-time conversational context, user intent, and project goals. It integrates information from the Knowledge Graph and Domain Services to build a rich, contextual understanding that informs AI agent behavior and cognitive artifact recommendations.

Technical Details

Interfaces

Protocols

• Internal gRPC - High-performance inter-service
• REST API (JSON) - External access

Data Model

Output is a dynamic JSON object representing the current context, conforming to ContextSnapshot within the UserInteractionFeedback schema.

Context Object Structure:

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{
  "sessionId": "uuid",
  "timestamp": "ISO-8601",
  "user": {
    "id": "user-123",
    "role": "Developer",
    "preferences": {...}
  },
  "task": {
    "type": "documentation",
    "project": "order-service",
    "phase": "review"
  },
  "entities": [
    {"type": "Customer", "id": "cust-456"},
    {"type": "Order", "id": "order-789"}
  ],
  "intent": "generate-api-docs",
  "semanticContext": {...}
}

API Specification

Endpoints

Request/Response

Analyze Context

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POST /context/analyze
{
  "sessionId": "session-abc",
  "userInput": "I need to update the order processing documentation",
  "currentTask": "documentation-review",
  "projectId": "order-service"
}

// Response
{
  "context": {
    "intent": "update-documentation",
    "entities": ["Order", "OrderProcessing"],
    "suggestedArtifacts": ["checklist", "api-reference"],
    "relevantRules": ["order-validation-rule"],
    "architecturalContext": {...}
  }
}

Error Codes

Dependencies

• Knowledge Graph Service: Semantic context retrieval
• Domain Services: Operational data access
• NLP Engine: Intent recognition, entity extraction

Error Handling

• Graceful handling of incomplete context data
• Fallback mechanisms for external service failures
• Partial results when some context sources unavailable
• Timeout handling for slow external calls

Performance Considerations

• Low-latency context analysis for real-time interactions
• Caching of frequently accessed contextual elements
• Parallel queries to multiple context sources
• Streaming response for progressive context building

Security Considerations

• Secure handling of sensitive user and project data
• Anonymization where appropriate
• Data retention policies for context history
• Access control for context analysis

Testing Strategy

• Unit Tests: NLP components (intent recognition, entity extraction)
• Integration Tests: Data retrieval from Knowledge Graph and Domain Services
• End-to-End Tests: Various conversational scenarios
• Performance Tests: Latency under concurrent requests